This notebook is an exercise in the Geospatial Analysis course. You can reference the tutorial at this link.
Introduction
You are part of a crisis response team, and you want to identify how hospitals have been responding to crash collisions in New York City.
Before you get started, run the code cell below to set everything up.
import math
import geopandas as gpd
import pandas as pd
from shapely.geometry import MultiPolygon
import folium
from folium import Choropleth, Marker
from folium.plugins import HeatMap, MarkerClusterYou’ll use the embed_map() function to visualize your maps.
Exercises
1) Visualize the collision data.
Run the code cell below to load a GeoDataFrame collisions tracking major motor vehicle collisions in 2013-2018.
collisions = gpd.read_file(r"C:\Users\LG PC\Desktop\data_mining\archive\NYPD_Motor_Vehicle_Collisions\NYPD_Motor_Vehicle_Collisions\NYPD_Motor_Vehicle_Collisions.shp")
collisions.head()| DATE | TIME | BOROUGH | ZIP CODE | LATITUDE | LONGITUDE | LOCATION | ON STREET | CROSS STRE | OFF STREET | … | CONTRIBU_2 | CONTRIBU_3 | CONTRIBU_4 | UNIQUE KEY | VEHICLE TY | VEHICLE _1 | VEHICLE _2 | VEHICLE _3 | VEHICLE _4 | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 07/30/2019 | 0:00 | BRONX | 10464 | 40.841100 | -73.784960 | (40.8411, -73.78496) | NaN | NaN | 121 PILOT STREET | … | Unspecified | NaN | NaN | 4180045 | Sedan | Station Wagon/Sport Utility Vehicle | Station Wagon/Sport Utility Vehicle | NaN | NaN | POINT (1043750.211 245785.815) |
| 1 | 07/30/2019 | 0:10 | QUEENS | 11423 | 40.710827 | -73.770660 | (40.710827, -73.77066) | JAMAICA AVENUE | 188 STREET | NaN | … | NaN | NaN | NaN | 4180007 | Sedan | Sedan | NaN | NaN | NaN | POINT (1047831.185 198333.171) |
| 2 | 07/30/2019 | 0:25 | NaN | NaN | 40.880318 | -73.841286 | (40.880318, -73.841286) | BOSTON ROAD | NaN | NaN | … | NaN | NaN | NaN | 4179575 | Sedan | Station Wagon/Sport Utility Vehicle | NaN | NaN | NaN | POINT (1028139.293 260041.178) |
| 3 | 07/30/2019 | 0:35 | MANHATTAN | 10036 | 40.756744 | -73.984590 | (40.756744, -73.98459) | NaN | NaN | 155 WEST 44 STREET | … | NaN | NaN | NaN | 4179544 | Box Truck | Station Wagon/Sport Utility Vehicle | NaN | NaN | NaN | POINT (988519.261 214979.320) |
| 4 | 07/30/2019 | 10:00 | BROOKLYN | 11223 | 40.600090 | -73.965910 | (40.60009, -73.96591) | AVENUE T | OCEAN PARKWAY | NaN | … | NaN | NaN | NaN | 4180660 | Station Wagon/Sport Utility Vehicle | Bike | NaN | NaN | NaN | POINT (993716.669 157907.212) |
5 rows × 30 columns
Use the “LATITUDE” and “LONGITUDE” columns to create an interactive map to visualize the collision data. What type of map do you think is most effective?
m_1 = folium.Map(location=[40.7, -74], zoom_start=11)
# Your code here: Visualize the collision data
HeatMap(data = collisions[["LATITUDE", "LONGITUDE"]], radius = 9).add_to(m_1)
m_12) Understand hospital coverage.
Run the next code cell to load the hospital data.
hospitals = gpd.read_file(r"C:\Users\LG PC\Desktop\data_mining\archive\nyu_2451_34494\nyu_2451_34494\nyu_2451_34494.shp")
hospitals.head()| id | name | address | zip | factype | facname | capacity | capname | bcode | xcoord | ycoord | latitude | longitude | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 317000001H1178 | BRONX-LEBANON HOSPITAL CENTER - CONCOURSE DIVI… | 1650 Grand Concourse | 10457 | 3102 | Hospital | 415 | Beds | 36005 | 1008872.0 | 246596.0 | 40.843490 | -73.911010 | POINT (1008872.000 246596.000) |
| 1 | 317000001H1164 | BRONX-LEBANON HOSPITAL CENTER - FULTON DIVISION | 1276 Fulton Ave | 10456 | 3102 | Hospital | 164 | Beds | 36005 | 1011044.0 | 242204.0 | 40.831429 | -73.903178 | POINT (1011044.000 242204.000) |
| 2 | 317000011H1175 | CALVARY HOSPITAL INC | 1740-70 Eastchester Rd | 10461 | 3102 | Hospital | 225 | Beds | 36005 | 1027505.0 | 248287.0 | 40.848060 | -73.843656 | POINT (1027505.000 248287.000) |
| 3 | 317000002H1165 | JACOBI MEDICAL CENTER | 1400 Pelham Pkwy | 10461 | 3102 | Hospital | 457 | Beds | 36005 | 1027042.0 | 251065.0 | 40.855687 | -73.845311 | POINT (1027042.000 251065.000) |
| 4 | 317000008H1172 | LINCOLN MEDICAL & MENTAL HEALTH CENTER | 234 E 149 St | 10451 | 3102 | Hospital | 362 | Beds | 36005 | 1005154.0 | 236853.0 | 40.816758 | -73.924478 | POINT (1005154.000 236853.000) |
Use the “latitude” and “longitude” columns to visualize the hospital locations.
m_2 = folium.Map(location=[40.7, -74], zoom_start=11)
# Your code here: Visualize the hospital locations
for row in hospitals.itertuples():
folium.Marker(
location=[row.latitude, row.longitude],
popup=row.address
).add_to(m_2)
m_23) When was the closest hospital more than 10 kilometers away?
Create a DataFrame outside_range containing all rows from collisions with crashes that occurred more than 10 kilometers from the closest hospital.
Note that both hospitals and collisions have EPSG 2263 as the coordinate reference system, and EPSG 2263 has units of meters.
# Your code here
hospitals_buffered = gpd.GeoDataFrame(geometry = hospitals['geometry']).buffer(10000)
union = hospitals_buffered.geometry.unary_union
outside_range = collisions.loc[~collisions["geometry"].apply(lambda x: union.contains(x))]The next code cell calculates the percentage of collisions that occurred more than 10 kilometers away from the closest hospital.
percentage = round(100*len(outside_range)/len(collisions), 2)
print("Percentage of collisions more than 10 km away from the closest hospital: {}%".format(percentage))Percentage of collisions more than 10 km away from the closest hospital: 15.12%4) Make a recommender.
When collisions occur in distant locations, it becomes even more vital that injured persons are transported to the nearest available hospital.
With this in mind, you decide to create a recommender that: - takes the location of the crash (in EPSG 2263) as input, - finds the closest hospital (where distance calculations are done in EPSG 2263), and - returns the name of the closest hospital.
def best_hospital(collision_location):
# Perform the spatial operation to find the nearest hospital for the collision location
nearest_hospital = hospitals.geometry.distance(collision_location).idxmin()
# Get the name of the nearest hospital
name = hospitals.loc[nearest_hospital, 'name']
return name
# Test your function: this should suggest CALVARY HOSPITAL INC
print(best_hospital(outside_range.geometry.iloc[0]))CALVARY HOSPITAL INC5) Which hospital is under the highest demand?
Considering only collisions in the outside_range DataFrame, which hospital is most recommended?
Your answer should be a Python string that exactly matches the name of the hospital returned by the function you created in 4).
# Your code here
highest_demand = 6) Where should the city construct new hospitals?
Run the next code cell (without changes) to visualize hospital locations, in addition to collisions that occurred more than 10 kilometers away from the closest hospital.
m_6 = folium.Map(location=[40.7, -74], zoom_start=11)
coverage = gpd.GeoDataFrame(geometry=hospitals.geometry).buffer(10000)
folium.GeoJson(coverage.geometry.to_crs(epsg=4326)).add_to(m_6)
HeatMap(data=outside_range[['LATITUDE', 'LONGITUDE']], radius=9).add_to(m_6)
folium.LatLngPopup().add_to(m_6)
m_6Click anywhere on the map to see a pop-up with the corresponding location in latitude and longitude.
The city of New York reaches out to you for help with deciding locations for two brand new hospitals. They specifically want your help with identifying locations to bring the calculated percentage from step 3) to less than ten percent. Using the map (and without worrying about zoning laws or what potential buildings would have to be removed in order to build the hospitals), can you identify two locations that would help the city accomplish this goal?
Put the proposed latitude and longitude for hospital 1 in lat_1 and long_1, respectively. (Likewise for hospital 2.)
Then, run the rest of the cell as-is to see the effect of the new hospitals. Your answer will be marked correct, if the two new hospitals bring the percentage to less than ten percent.
# Proposed location for hospital 1
lat_1 = 40.75
long_1 = -73.95
# Proposed location for hospital 2
lat_2 = 40.8
long_2 = -73.98
# Do not modify the code below this line
try:
new_df = pd.DataFrame(
{'Latitude': [lat_1, lat_2],
'Longitude': [long_1, long_2]})
new_gdf = gpd.GeoDataFrame(new_df, geometry=gpd.points_from_xy(new_df.Longitude, new_df.Latitude))
new_gdf.crs = {'init' :'epsg:4326'}
new_gdf = new_gdf.to_crs(epsg=2263)
# get new percentage
new_coverage = gpd.GeoDataFrame(geometry=new_gdf.geometry).buffer(10000)
new_my_union = new_coverage.geometry.unary_union
new_outside_range = outside_range.loc[~outside_range["geometry"].apply(lambda x: new_my_union.contains(x))]
new_percentage = round(100*len(new_outside_range)/len(collisions), 2)
print("(NEW) Percentage of collisions more than 10 km away from the closest hospital: {}%".format(new_percentage))
# make the map
m = folium.Map(location=[40.7, -74], zoom_start=11)
folium.GeoJson(coverage.geometry.to_crs(epsg=4326)).add_to(m)
folium.GeoJson(new_coverage.geometry.to_crs(epsg=4326)).add_to(m)
for idx, row in new_gdf.iterrows():
Marker([row['Latitude'], row['Longitude']]).add_to(m)
HeatMap(data=new_outside_range[['LATITUDE', 'LONGITUDE']], radius=9).add_to(m)
folium.LatLngPopup().add_to(m)
display(m)
except:
Nonec:\Users\LG PC\anaconda3\envs\min\Lib\site-packages\pyproj\crs\crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6
in_crs_string = _prepare_from_proj_string(in_crs_string)
(NEW) Percentage of collisions more than 10 km away from the closest hospital: 15.0%Congratulations!
You have just completed the Geospatial Analysis micro-course! Great job!
Have questions or comments? Visit the course discussion forum to chat with other learners.